SoroushVahidi/scidocs huggingface dataset:
An accurate algebraic solution for moving source location using TDOA and FDOA measurements
(2004)

This paper proposes an algebraic solution for the position and velocity of a moving source using the time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) of a signal received at a number of receivers. The method employs several weighted least-squares minimizations only and does not require initial solution guesses to obtain a location estimate. It does not have the initialization and local convergence problem as in the conventional linear iterative method. The estimated accuracy of the source position and velocity is shown to achieve the Crame/spl acute/r-Rao lower bound for Gaussian TDOA and FDOA noise at moderate noise level before the thresholding effect occurs. Simulations are included to examine the algorithm's performance and compare it with the Taylor-series iterative method.
Towards dropout training for convolutional neural networks
(2015)

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.
Techniques and Programs Used in Bidding and Playing phases of Imperfect Information Game – an Overview
(2012)

Bridge is an international imperfect information game played with similar rules all over the world and it is played by millions of players. It is an intelligent game; it increases creativity and knowledge of human mind. Many of the researchers analyses the Bridge bidding and playing phases, and they developed many programs for getting better results. The programs were performing well and it is also matter of time before the computer beats most human bridge players. As such, the researchers mainly focused on the techniques and computer programs which were used in bridge bidding
High-Gain Reconfigurable Sectoral Antenna Using an Active Cylindrical FSS Structure
(2011)

A novel design of a high-gain reconfigurable sectoral antenna using an active cylindrical frequency selective surface (FSS) structure is presented. The FSS structure consists of metallic discontinuous strips with PIN diodes in their discontinuities, and it is placed cylindrically around an omnidirectional electromagnetically coupled coaxial dipole (ECCD) array. The cylindrical FSS structure is divided into two semi-cylinders. By controlling the state of diodes in each semi-cylinder, a directive radiation pattern is obtained that can be swept in the entire azimuth plane. The effect of the diode-state configuration and the radius of the cylindrical structure are carefully studied to obtain an optimum sectoral radiation pattern. In addition, a solution for increasing the matching bandwidth of the antenna is also proposed. An experimental prototype was fabricated, and the measured results show a beamwidth of 20° in elevation and 70° in the azimuth plane at 2.1 GHz with a gain of 13 dBi. With these features, the proposed antenna is suitable for base-station applications in wireless communication systems.
Methodology for customer relationship management
(2006)

Customer relationship management (CRM) is a customer-focused business strategy that dynamically integrates sales, marketing and customer care service in order to create and add value for the company and its customers. This change towards a customer-focused strategy is leading to a strong demand for CRM solutions by companies. However, in spite of companies interest in this new management model, many CRM implementations fail. One of the main reasons for this lack of success is that the existing methodologies being used to approach a CRM project are not adequate, since they do not satisfactorily integrate and complement the strategic and technological aspects of CRM. This paper describes a formal methodology for directing the process of developing and implementing a CRM System that considers and integrates various aspects, such as defining a customer strategy, re-engineering customer-oriented business processes, human resources management, the computer system, management of change and continuous improvement. 2005 Elsevier Inc. All rights reserved.
Music Education and Its Impact on Students with Special Needs
(2018)

Part of the Curriculum and Instruction Commons, Curriculum and Social Inquiry Commons, Disability and Equity in Education Commons, Educational Methods Commons, Elementary Education and Teaching Commons, Higher Education and Teaching Commons, Junior High, Intermediate, Middle School Education and Teaching Commons, Other Educational Administration and Supervision Commons, Other Teacher Education and Professional Development Commons, Pre-Elementary, Early Childhood, Kindergarten Teacher Education Commons, Scholarship of Teaching and Learning Commons, and the Special Education Administration Commons Survey: Let us know how this paper benefits you.
Composite Scale of Morningness: psychometric properties, validity with Munich ChronoType Questionnaire and age/sex differences in Poland.
(2015)

The present study aimed at testing psychometric properties of the Composite Scale of Morningness (CSM) and validating it with mid sleep on free days (MSF) derived from the Munich ChronoType Questionnaire (MCTQ) in Poland, along with analyzing age and sex differences in the CSM and MSF. A sample of 952 Polish residents (62.6% females) aged between 13 and 46 was tested. Additionally, a sample of 33 university students were given MCTQ and filled in a sleep diary for 8 days. MSF derived from MCTQ was related to the one from sleep diary (r=.44). The study revealed good reliability of the CSM (α=.84) and its validity: greater morningness preference was associated with earlier MSF from MCTQ (r=-.52). CSM scores were distributed over its full range, with a mean of 34, and did not differ between sexes, although females were earlier than males by 23minutes in MSF. Regarding age, eveningness estimated with both CSM and MSF was greatest in subjects aged 16-18years, and a shift toward eveningness during puberty and a shift back toward morningness in older age was observed. The Polish version of the CSM consisted of two components of morningness. Cutoff scores were: for evening types (lower 10%) 24 or less, for morning types (upper 10%) 43 or more. The Polish CSM presents good psychometric properties, which are similar to those reported in other language versions, and also presents sex/age patterns similar to those found previously.
Analysis of different PM machines with concentrated windings and flux barriers in stator core
(2014)

The new stator structure with magnetic flux-barriers in the stator yoke or tooth region represents an efficient method for reducing the sub-harmonics of electric machines with fractional slots, tooth-concentrated windings. In this paper the both flux-barriers techniques are considered during the analysis of different PM machines. The 12-teeth single-layer and double-layer concentrated winding in combination with a 10-poles and 14-poles PM rotor are investigated. For the all machine topologies the new stator design is used to improve their performances and characteristics. The flux-barrier effects on the main machine parameters, such as in the air-gap flux density harmonics, dq-machine parameters, characteristic currents, electromagnetic torque, and so on, are studied carefully. Comparisons performed with the analogous conventional machines (with conventional stator) show that, the new stator design offers significant advantages.
Solving the Multiple Instance Problem with Axis-Parallel Rectangles
(1997)

The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.
A survey of cost-sensitive decision tree induction algorithms
(2013)

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy-based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field.
Tree-Based Batch Mode Reinforcement Learning
(2005)

Reinforcement learning aims to determine an optimal contro l policy from interaction with a system or from observations gathered from a system. In batch mode, i t can be achieved by approximating the so-calledQ-function based on a set of four-tuples (xt ,ut , rt ,xt+1) wherext denotes the system state at timet, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the contro l policy from this Q-function. The Q-function approximation may be obtained from the limit of a s equence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) a d two newly proposed ensemble algorithms, namelyextremelyandtotally randomized trees. We study their performances on several examples and find that the ensemble methods based on regressi on trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accurac y results are provided by the extremely randomized trees.
Visual exploration and evaluation of climate-related simulation data
(2007)

Large, heterogeneous volumes of simulation data are calculated and stored in many disciplines, e.g. in climate and climate impact research. To gain insight, current climate analysis applies statistical methods and model sensitivity analyzes in combination with standard visualization techniques. However, there are some obstacles for researchers in applying the full functionality of sophisticated visualization, exploiting the available interaction and visualization functionality in order to go beyond data presentation tasks. In particular, there is a gap between available and actually applied multi-variate visualization techniques. Furthermore, visual data comparison of simulation (and measured) data is still a challenging task. Consequently, this paper introduces a library of visualization techniques, tailored to support exploration and evaluation of climate simulation data. These techniques are integrated into the easy-to-use visualization framework SimEnvVis - designed as a front-end user interface to a simulation environment - which provides a high level of user support generating visual representations.
Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation
(2014)

The sheer volume of scholarly publications available online significantly challenges how scholars retrieve the new information available and locate the candidate reference papers. While classical text retrieval and pseudo relevance feedback (PRF) algorithms can assist scholars in accessing needed publications, in this study, we propose an innovative publication ranking method with PRF by leveraging a number of meta-paths on the heterogeneous bibliographic graph. Different meta-paths on the graph address different ranking hypotheses, whereas the pseudo-relevant papers (from the retrieval results) are used as the seed nodes on the graph. Meanwhile, unlike prior studies, we propose "restricted meta-path" facilitated by a new context-rich heterogeneous network extracted from full-text publication content along with citation context. By using learning-to-rank, we integrate 18 different meta-path-based ranking features to derive the final ranking scores for candidate cited papers. Experimental results with ACM full-text corpus show that meta-path-based ranking with PRF on the new graph significantly (p < 0.0001) outperforms text retrieval algorithms with text-based or PageRank-based PRF.
Convex Formulations for Fair Principal Component Analysis
(2018)

Though there is a growing literature on fairness for supervised learning, incorporating fairness into unsupervised learning has been less well-studied. This paper studies fairness in the context of principal component analysis (PCA). We first define fairness for dimensionality reduction, and our definition can be interpreted as saying a reduction is fair if information about a protected class (e.g., race or gender) cannot be inferred from the dimensionality-reduced data points. Next, we develop convex optimization formulations that can improve the fairness (with respect to our definition) of PCA and kernel PCA. These formulations are semidefinite programs, and we demonstrate their effectiveness using several datasets. We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.
An "AI readability" Formula for French as a Foreign Language
(2012)

This paper present a new readability formula for French as a foreign language (FFL), which relies on 46 textual features representative of the lexical, syntactic, and semantic levels as well as some of the specificities of the FFL context. We report comparisons between several techniques for feature selection and various learning algorithms. Our best model, based on support vector machines (SVM), significantly outperforms previous FFL formulas. We also found that semantic features behave poorly in our case, in contrast with some previous readability studies on English as a first language.
Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series
(2017)

Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise.
Apple disease classification using color, texture and shape features from images
(2016)

The presence of diseases in several kinds of fruits is the major factor of production and the economic degradation of the agricultural industry worldwide. An approach for the apple disease classification using color, texture and shape based features is investigated and experimentally verified in this paper. The primary steps of the introduced image processing based method is as follows; 1) infected fruit part detection is done with the help of K-Means clustering method, 2) color, texture and shape based features are computed over the segmented image and combined to form the single descriptor, and 3) multi-class Support Vector Machine is used to classify the apples into one of the infected or healthy categories. Apple fruit is taken as the test case in this study with 3 categories of diseases, namely blotch, rot and scab as well as healthy apples. The experimentation points out that the introduced method based is better as compared to the individual features. It also point out that shape feature is not better suited for this purpose.
Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification
(2017)

Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.
Exploring the musical taste of expert listeners: musicology students reveal tendency toward omnivorous taste
(2015)

Musicology students are engaged with music on an academic level and usually have an extensive musical background. They have a considerable knowledge of music history and theory and listening to music may be regarded as one of their primary occupations. Taken together, these factors qualify them as ≫expert listeners≪, who may be expected to exhibit a specific profile of musical taste: interest in a broad range of musical styles combined with a greater appreciation of ≫sophisticated≪ styles. The current study examined the musical taste of musicology students as compared to a control student group. Participants (n = 1003) completed an online survey regarding the frequency with which they listened to 22 musical styles. A factor analysis revealed six underlying dimensions of musical taste. A hierarchical cluster analysis then grouped all participants, regardless of their status, according to their similarity on these dimensions. The employed exploratory approach was expected to reveal potential differences between musicology students and controls. A three-cluster solution was obtained. Comparisons of the clusters in terms of musical taste revealed differences in the listening frequency and variety of appreciated music styles: the first cluster (51% musicology students/27% controls) showed the greatest musical engagement across all dimensions although with a tendency toward ≫sophisticated≪ musical styles. The second cluster (36% musicology students/46% controls) exhibited an interest in ≫conventional≪ music, while the third cluster (13% musicology students/27% controls) showed a strong liking of rock music. The results provide some support for the notion of specific tendencies in the musical taste of musicology students and the contribution of familiarity and knowledge toward musical omnivorousness. Further differences between the clusters in terms of social, personality, and sociodemographic factors are discussed.
Markov Logic Networks for Optical Chemical Structure Recognition
(2014)

Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image data sets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.dinfo.unifi.it.
An LTE Base-Station Magnetoelectric Dipole Antenna with Anti-Interference Characteristics and Its MIMO System Application
(2015)

In this research, a wideband long-term evolution (LTE) base-station antenna with flexible band-notch characteristics is introduced by two window-type slots etched on radiation patches of the magnetoelectric dipole. The presented antenna can provide a band-notch range with an impedance bandwidth of 2.39-2.52 GHz in the band of 1.825-2.925 GHz, which well applies the wideband antenna for LTE with the good anti-interference from wireless local area network narrow communication band. The typical parameters are investigated to show that the antenna with anti-interference characteristics exhibits large scope band-notch adjustability in the whole wideband region. Based on above element, an LTE base station antenna multiple-input, multiple-output system with anti-interference characteristics is investigated and measured to show its good performances: anti-interference, high isolation, high gain, unidirectional radiation, low back radiation, and low cross-polarization.